{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrate how to define a cross-validation, a classifier, to classify a set a features and to visualize results.\n",
    "Documentation: https://etiennecmb.github.io/classification.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# u can use %matplotlib notebook, but there is some bugs with xticks and title\n",
    "\n",
    "from brainpipe.classification import *\n",
    "from brainpipe.visual import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a random dataset\n",
    "We are going to create a random dataset for a 2 class problem, n_trials pear class and n_feature. The quality of decoding of features will be increasing with the ranking, meaning that the first feature is going to be a bad one, the second alittle bit better, the third..., the last, the best one.\n",
    "## Dataset settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_trials = 100    # Number of trials pear class\n",
    "n_features = 15   # Number of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GAAAAekJyDQAAAPSE5BoAAADoCck1AAAA0BOSawAAAKAnJNcAAABAT0iuAQAAgJ5UT65t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\nkmsAAACgJyTXAAAAQE/+P7WLVIpQ/ufJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1954b87128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spread = np.linspace(0, 0.3, n_features)\n",
    "class1 = np.random.uniform(size=(n_trials, n_features)) + spread\n",
    "class2 = np.random.uniform(size=(n_trials, n_features)) - spread\n",
    "x = np.concatenate((class1, class2), axis=0)\n",
    "y = np.ravel([[k]*n_trials for k in np.arange(2)])\n",
    "\n",
    "plt.figure(0, figsize=(12,6))\n",
    "plt.boxplot(x);\n",
    "rmaxis(plt.gca(), ['top', 'right']);\n",
    "plt.xlabel('Features'), plt.ylabel('Values');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification\n",
    "## Define a classifier\n",
    "We are going to create a classifier: a Support Vector Machine with a rbf kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = 'svm'\n",
    "kern='rbf'\n",
    "clf_obj = defClf(y, clf=model, kern=kern)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a cross-validation\n",
    "Here, we create a five times 10-stratified cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rep = 5\n",
    "cvmodel = 'skfold'\n",
    "cv_obj = defCv(y, cvtype=cvmodel, rep=rep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the classification object\n",
    "Basically, this object link the cross-validation and the classifier together and provide a bundle of functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cla_obj = classify(y, clf=clf_obj, cvtype=cv_obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Be sure of your settings\n",
    "If you want to be sure of your current settings, you can print embedded tables\n",
    "### Classifier and cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chance (theorical, %)</th>\n",
       "      <th>Class</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Cross-validation</th>\n",
       "      <th>Repetition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>Support Vector Machine (kernel=rbf)</td>\n",
       "      <td>5-times, 10 Stratified k-folds</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chance (theorical, %)   Class                           Classifier  \\\n",
       "0                   50.0  [0, 1]  Support Vector Machine (kernel=rbf)   \n",
       "\n",
       "                 Cross-validation  Repetition  \n",
       "0  5-times, 10 Stratified k-folds           5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cla_obj.info.clfinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "### Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chance (binomial, %)</th>\n",
       "      <th>Chance (theorical, %)</th>\n",
       "      <th>Class</th>\n",
       "      <th>N-class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'p_0.05': 56.0, 'p_0.01': 58.0, 'p_0.001': 61.0}</td>\n",
       "      <td>50.0</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Chance (binomial, %)  Chance (theorical, %)  \\\n",
       "0  {'p_0.05': 56.0, 'p_0.01': 58.0, 'p_0.001': 61.0}                   50.0   \n",
       "\n",
       "    Class  N-class  \n",
       "0  [0, 1]        2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cla_obj.info.statinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the classification\n",
    "Here, we classify each feature separatly and estimate the significiancy of each one using 20 permutations (randomize vector label 20 times which to at least p<0.05 p-values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "da, pvalue, daperm = cla_obj.fit(x, n_perm=20, method='label_rnd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display features information and save to an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Settings</th>\n",
       "      <th colspan=\"5\" halign=\"left\">SVM-rbf / 5-rep_10-skfold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Results</th>\n",
       "      <th>DA (%)</th>\n",
       "      <th>STD (+/-)</th>\n",
       "      <th>p-values (Binomial)</th>\n",
       "      <th>p-values (Permutations)</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.9</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.7376888221388045</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.6</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.051819519218933796</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.051819519218933796</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0011397192509029486</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.3</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.0018174739762649716</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.020018595806698514</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>63.2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>8.209262515823657e-05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>65.2</td>\n",
       "      <td>0.93</td>\n",
       "      <td>6.928725786226053e-06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64.4</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.48648673960572e-05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66.1</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.7735890870396176e-06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.149597415154972e-10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>71.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0076962314874436e-10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>71.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0076962314874436e-10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>77.2</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.2212453270876722e-15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>80.9</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.1102230246251565e-16</td>\n",
       "      <td>0.05</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Settings SVM-rbf / 5-rep_10-skfold                                    \\\n",
       "Results                     DA (%) STD (+/-)     p-values (Binomial)   \n",
       "0                             47.9      2.22      0.7376888221388045   \n",
       "1                             55.6      0.49    0.051819519218933796   \n",
       "2                             55.8       0.6    0.051819519218933796   \n",
       "3                             60.5       1.0   0.0011397192509029486   \n",
       "4                             60.3      0.68   0.0018174739762649716   \n",
       "5                             57.0      0.84    0.020018595806698514   \n",
       "6                             63.2      0.24   8.209262515823657e-05   \n",
       "7                             65.2      0.93   6.928725786226053e-06   \n",
       "8                             64.4      0.73    2.48648673960572e-05   \n",
       "9                             66.1      0.58  1.7735890870396176e-06   \n",
       "10                            71.2       0.4   5.149597415154972e-10   \n",
       "11                            71.7       0.4  2.0076962314874436e-10   \n",
       "12                            71.7       0.4  2.0076962314874436e-10   \n",
       "13                            77.2      0.51  1.2212453270876722e-15   \n",
       "14                            80.9      0.58  1.1102230246251565e-16   \n",
       "\n",
       "Settings                                \n",
       "Results  p-values (Permutations) Group  \n",
       "0                           0.45     0  \n",
       "1                           0.15     1  \n",
       "2                           0.05     2  \n",
       "3                           0.05     3  \n",
       "4                           0.05     4  \n",
       "5                           0.05     5  \n",
       "6                           0.05     6  \n",
       "7                           0.05     7  \n",
       "8                           0.05     8  \n",
       "9                           0.05     9  \n",
       "10                          0.05    10  \n",
       "11                          0.05    11  \n",
       "12                          0.05    12  \n",
       "13                          0.05    13  \n",
       "14                          0.05    14  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export every information to ecel :\n",
    "filename = 'classification_demo.xlsx'\n",
    "cla_obj.info.to_excel(filename)\n",
    "\n",
    "# Display informations about features :\n",
    "cla_obj.info.featinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot your results\n",
    "### Plot decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19547402b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpI4M1ZIkSVJHhmpJkiSpI0O1JEmS1JGhWpIkSerIUC1JkiR1ZKiWJEmSOjJU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1954740a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(12,8))\n",
    "\n",
    "cla_obj.daplot(da, daperm=daperm, chance_method='perm', rmax=['top', 'right'],\n",
    "               dpax=['bottom', 'left'], cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot confusion matrix of the last feature only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f195059be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get each confusion matrix of each feature :\n",
    "cm = cla_obj.cm()\n",
    "\n",
    "# Plot the confusion matrix of the mast feature (-1):\n",
    "fig2 = plt.figure(2, figsize=(6, 6))\n",
    "cla_obj.cmplot(fig2, cm[-1, ...], fignum=2, figtitle='My figure', subspace={'top':0.85},\n",
    "               title='Example of a confusion matrix', vmin=16, vmax=83);\n",
    "\n",
    "# Save the plot :\n",
    "fig2.savefig('My_confusion_matrix.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to grouping\n",
    "## The group parameter and multi-features\n",
    "Previously, we saw how to classify each feature separatly. Now we are going to see how to group features. In this example, we defined 15 features. So, we are going to define 3 groups features\n",
    "- 'Group1: the bad one': the 5 first\n",
    "- 'Group2: the middle one': the 3 following\n",
    "- 'Group3: the best one': the last 7 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define the group parameter :\n",
    "grp = ['Group1: the bad one']*5 + ['Group2: the middle one']*3 + ['Group3: the best one']*7\n",
    "\n",
    "# Define a new classification object for this example :\n",
    "cla_obj2 = classify(y, clf='lda', cvtype='sss', cvArg={'rep':30, 'n_folds':10})\n",
    "\n",
    "# Run classification, not on each feature, but on each group of features:\n",
    "da2, pvalue2, daperm2 = cla_obj2.fit(x, grp=grp, method='label_rnd', n_perm=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the grouping decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Settings</th>\n",
       "      <th colspan=\"5\" halign=\"left\">LDA / 30-rep_10-sss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Results</th>\n",
       "      <th>DA (%)</th>\n",
       "      <th>STD (+/-)</th>\n",
       "      <th>p-values (Binomial)</th>\n",
       "      <th>p-values (Permutations)</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66.05</td>\n",
       "      <td>2.81</td>\n",
       "      <td>1.7735890870396176e-06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Group1: the bad one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.61666666666666</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.0076962314874436e-10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Group2: the middle one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.66666666666667</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.1102230246251565e-16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Group3: the best one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Settings LDA / 30-rep_10-sss                                    \\\n",
       "Results               DA (%) STD (+/-)     p-values (Binomial)   \n",
       "0                      66.05      2.81  1.7735890870396176e-06   \n",
       "1          71.61666666666666       3.8  2.0076962314874436e-10   \n",
       "2          98.66666666666667      0.77  1.1102230246251565e-16   \n",
       "\n",
       "Settings                                                  \n",
       "Results  p-values (Permutations)                   Group  \n",
       "0                           0.02     Group1: the bad one  \n",
       "1                           0.02  Group2: the middle one  \n",
       "2                           0.02    Group3: the best one  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7kZuZ2YDSKJE3q1ofIelQUnLdg3Rt/GpJu/ZQTIOBRSQNITWgexb4GDA+zx8P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\nC/PlizkNigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1952026ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(3, figsize=(8, 6))\n",
    "cla_obj2.daplot(da2, cmap='Spectral_r', ylim=[45, 100], chance_method='perm',\n",
    "                daperm=daperm2, chance_color='darkgreen');\n",
    "cla_obj2.info.featinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the classification is applied on each group. The mf parameter of the fit() function is just a shortcut to say that all features have to be consider together. \n",
    "U can use the grp parameter for:\n",
    "- Grouping features together\n",
    "- Labelize each one/group to have nice tables and plot"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
